Marginal Cost of Energy Volatility in the Brazilian Interconnected Power System

From 2018 to 2022, on average, 70% of the Brazilian effective electric generation was produced by hydropower, 10% by wind power, and 20% by thermal power plants. Over the last five years, Brazil suffered from a series of severe droughts. As a result, hydropower generation was reduced, but demand growth was also declined as results of the COVID-19 pandemic and economic recession. From 2012 to 2022, the Brazilian reservoir system operated with, on average, only 40% of the active storage, but storage recovered to normal levels in the first three months of 2022. Despite large capacity of storage reservoirs, high volatility of the marginal cost of energy was observed in recent years. In this paper, we used two optimization models, NEWAVE and HIDROTERM for our study. These two models were previously developed for mid-range planning of the operation of the Brazilian interconnected power system. We used these two models to optimize the operation and compared the results with observed operational records for the period of 2018-2022. NEWAVE is a stochastic dual dynamic programming model which aggregates the system into four subsystems and 12 equivalent reservoirs. HIDROTERM is a nonlinear programming model that considers each of the 167 individual hydropower plants of the system. The main purposes of the comparison are to assess cooperation opportunities with the use of both models and better understand the impacts of increasing uncertainties, seasonality of inflows and winds, demand forecasts, decisions about storage in reservoirs, and thermal production on energy prices. © World Environmental and Water Resources Congress 2023.All rights reserved

Assessing the Impact of Disruptive Events on Urban Mobility: A Case Study of Chicago Taxis during COVID-19

The rise of Transportation Network Companies (TNCs) over the last decade has significantly disrupted the taxi industry. Studies have shown that taxi ridership has plummeted, and their capacity utilization rates are lower than 50% in five major U.S. cities. Additionally, the COVID-19 pandemic has dealt a severe blow to the already struggling taxi industry. To monitor the evolution of the taxi industry and its impacts on society, our study evaluates changes in the utilization rates, fuel consumption, and emissions among Chicago taxis, using taxi data with rich information on trip profiles from pre-pandemic and pandemic times. Our findings indicate that the taxi utilization rate decreased during the pandemic. While fuel consumption and emissions per kilometer decreased thanks to the reduced traffic during the pandemic, the overall fuel consumption and emissions increased due to increased deadhead travel. The methods developed in this study can be applied to monitor and evaluate the impact of future disruptive events on urban mobility and transportation systems more effectively. By utilizing mobility data to better understand transportation systems, we can develop more efficient, sustainable, and resilient mobility solutions for smart cities. © 2023 ACM.

Modeling the influence of the information domain on countermeasure effectiveness in case of COVID-19

A common way to model an epidemic — restricted to contagion aspects only — is a modification of the Kermack-McKendrick SIR Epidemic model (SIR model) with differential equations. (Mis-)Information about epidemics may influence the behavior of the people and thus the course of epidemics as well. We have thus coupled an extended SIR model of the COVID-19 pandemic with a compartment model of the (mis-)information-based attitude of the population towards epidemic countermeasures. The resulting combined model is checked concerning basic plausibility properties like positivity and boundedness. It is calibrated using COVID-19 data from RKI and attitude data provided by the COVID-19 Snapshot Monitoring (COSMO) study. The values of parameters without corresponding observation data have been determined using an L2-fit under mild additional assumptions. The predictions of the calibrated model are essentially in accordance with observations. An uncertainty analysis of the model shows, that our results are in principle stable under measurement errors. We also assessed the scale, at which specific parameters can influence the evolution of epidemics. Another result of the paper is that in a multi-domain epidemic model, the notion of controlled reproduction number has to be redefined when being used as an indicator of the future evolution of epidemics.

Measuring Health Systems Resilience: A Comparative Study of Turkey's Health System during COVID-19 Pandemic

The recent outbreak of the COVID-19 pandemic has drawn significant attention to the topic of health-system resilience. Many countries have taken certain measures to deal with the negative outcomes of the pandemic and to improve their health systems. Having a resilient health system during pandemics ensures the continuity and success of healthcare services. Resilience, as a concept, represents a proactive rather than a reactive approach to overcoming the negative outcomes of disasters. Understanding the characteristics of a resilient health system will help to strengthen the health systems for future pandemics or any other disasters. In this research project, characteristics of resilient health systems are investigated using a framework based on three main dimensions of systems resilience: (1) a system's capability to decrease its level of vulnerability to expected and unexpected disruptive events, (2) its ability to change itself and adapt to the changing environment;(3) its ability to recover in the least possible time in case of a disruptive event. Based on this framework, four attributes of resilience are identified, namely agility, adaptability, flexibility, and vulnerability. Further, these attributes of resilience are evaluated using country-specific COVID-19-related qualitative and quantitative data from Turkey and compared with several other countries. Suggestions and further recommendations are provided on how to measure and improve the resiliency of health systems for future pandemics. © 2022 IEEE.

Uncertainty Analysis and Optimization Modeling with Application to Supply Chain Management: A Systematic Review

In recent years, there have been frequent cases of impact on the stable development of supply chain economy caused by uncertain events such as COVID-19 and extreme weather events. The creation, management, and impact coping techniques of the supply chain economy now face wholly novel requirements as a result of the escalating level of global uncertainty. Although a significant literature applies uncertainty analysis and optimization modeling (UAO) to study supply chain management (SCM) under uncertainty, there is a lack of systematic literature review and research classification. Therefore, in this paper, 121 articles published in 44 international academic journals between 2015 and 2022 are extracted from the Web of Science database and reviewed using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Bibliometric analysis and CiteSpace software are used to identify current developments in the field and to summarize research characteristics and hot topics. The selected published articles are classified and analyzed by author name, year of publication, application area, country, research purposes, modeling methods, research gaps and contributions, research results, and journals to comprehensively review and evaluate the SCM in the application of UAO. We find that UAO is widely used in SCM under uncertainty, especially in the field of decision-making, where it is common practice to ly model the decision problem to obtain scientific decision results. This study hopes to provide an important and valuable reference for future research on SCM under uncertainty. Future research could combine uncertainty theory with supply chain management segments (e.g., emergency management, resilience management, and security management), behavioral factors, big data technologies, artificial intelligence, etc. [ FROM AUTHOR] Copyright of Mathematics (2227-7390) is the property of MDPI and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Uncertainty management in electricity demand forecasting with machine learning and ensemble learning: Case studies of COVID-19 in the US metropolitans

Improving load forecasting is becoming increasingly crucial for power system management and operational research. Disruptive influences can seriously impact both the supply and demand sides of power. This work examines the impact of the coronavirus on power usage in two US states from January 2020 to December 2020. A wide range of machine learning (ML) algorithms and ensemble learning are employed to conduct the analysis. The findings showed a surprising increase in monthly power use changes in Florida and Texas during the COVID-19 pandemic, in contrast to New York, where usage decreased over the same period. In Texas, the quantity of power usage rises from 2% to 6% practically every month, except for September, when it decreased by around 1%. For Florida, except for May, which showed a fall of roughly 2.5%, the growth varied from 2.5% to 7.5%. This indicates the need for more extensive research into such systems and the applicability of adopting groups of algorithms in learning the trends of electric power demand during uncertain events. Such learning will be helpful in forecasting future power demand changes due to especially public health-related scenarios. © 2023 Elsevier Ltd

Risk Modeling Framework for Strategic and Operational Intervention to Enhance the Effectiveness of a Closed-Loop Supply Chain

In a closed-loop supply chain (CLSC), acquiring end-of-life vehicles (ELVs) and their components from both primary and secondary markets has posed a huge uncertainty and risk. Moreover, the constant supply of ELV components with minimization of cost and exploitation of natural resources is another pressing challenge. To address the issues, the present study has developed a risk simulation framework to study market uncertainty/risk in a CLSC. In the first phase of the framework, a total of 12 important variables are identified from the existing studies. The total interpretive structural model (TISM) is used to develop a causal relationship network among the variables. Then, Matriced Impacts Cruoses Multiplication Applique a un Classement is used for determining the nature of relationships (i.e., driving or dependence power). In the second phase, the relationship of TISM is used to derive a Bayesian belief network model for determining the level of risks (i.e., high, medium, and low) associated with the CLSC through the generation of conditional probabilities across 1) multi-, 2) single-, and 3) without-parent nodes. The study findings will help decision-makers in adopting strategic and operational interventions to increase the effectiveness and resiliency of the network. Furthermore, it will help practitioners to make decisions on change management implementation for stakeholders'performance audits on the attributes of the ELV recovery program and developing resilience in the CLSC network. Overall, the present study holistically contributes to a broader investigation of the implications of strategic decisions in automobile manufacturers and resellers. IEEE

The stratified multi criteria decision making method: An extended version

It often occurs that after a multi-criteria decision is made, the decision maker becomes unsure as to whether they have made the best decision. This doubt arises because the criteria being considered do not carry the same weightings. This instability is relevant to the consideration of possible future events, such as a possible recession following the COVID-19 outbreak, which may affect the criteria weightings. The stratified multi-criteria decision-making method (SMCDM) has been proposed to address this issue. This method suggests the consideration of a number of states in the decision-making process. In each state, the weightings of the criteria are different depending on which event or which combination of events are being considered. The states are associated with transition probabilities that are used to compute the optimal weightings of the criteria. This paper suggests approaches to compute the transition probabilities. Moreover, the consideration of several events in SMCDM results in a great number of states and this would be a time consuming and error prone process. Hence, the incremental enlargement characteristic of the concept of stratification (CST) is added to SMCDM in order to reduce the large numbers of states to a manageable quantity. IEEE

Development and Application of a Specialized Set of Engineering Tools to Enhance Enterprise and Supply Chain Resilience

Complexity, dynamism, sudden changes, and disruptive events (COVID-19, Ukraine war, etc.) have become the norm in the current business world. Companies and their related supply chains are trying to adapt to a business reality fed by disruptive events to try to guarantee their survival in the long term. It is essential to highlight that some disruptive events are more predictable than others. However, even for the non-predictable events, early symptoms will facilitate their detection. Thus, it is critical to provide quantitative tools to identify patterns and warn companies to activate resilience plans and preventive actions. These tools should include features such as multivariate analysis for pattern recognition, disruptive events prediction, and prioritization of the preventive actions related to each disruptive event to support companies in enhancing their resilience capacity. In addition, the entire organization must be committed and convinced of the benefits that improved resilience will bring. For this reason, it is also critical to develop mechanisms to make workers aware of the importance of being resilient and promote the implementation of the resilience dimension in their quality systems, which is an opportunity for an organization to get formally certified in this area. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

How Does Firm-Level Economic Policy Uncertainty Affect Corporate Innovation? Evidence from China

Innovation is the main driving force of the sustainable development of enterprises. Economic policy uncertainty has increased dramatically in recent years due to events such as COVID-19, which will alter the business environment of enterprises and ultimately affect their innovation behavior. How economic policy uncertainty will affect corporate innovation has become a crucial topic, but empirical studies have not reached consistent conclusions, and few have noted the heterogeneity of different firms' perceptions of uncertainty. This study used a textual analysis approach to create firm-level economic policy uncertainty indicators from the texts of annual reports of Chinese A-share listed firms. Based on the effectiveness of our measure of economic policy uncertainty, we further examined its impact on firm innovation. We find that our uncertainty measure has negative effects on enterprise innovation activity, and this negative impact is more significant among non-state-owned enterprises, and firms with higher financial constraints and lower government subsidies. We extend the measurement of economic policy uncertainty from the micro level and provide some suggestions for policymakers at the macro level. In the period of increasing uncertainty in the external environment, the government should try to maintain the stability and transparency of economic policies, and provide more targeted policy support to enterprises, such as by broadening their financing channels and providing innovation subsidies. © 2023 by the authors.

The time-varying impact of uncertainty on oil market fear: Does climate policy uncertainty matter?

Implied volatility index is a popular proxy for market fear. This paper uses the oil implied volatility index (OVX) to investigate the impact of different uncertainty measures on oil market fear. Our uncertainty measures consider multiple perspectives, specifically including climate policy uncertainty (CPU), geopolitical risk (GPR), economic policy uncertainty (EPU), and equity market volatility (EMV). Based on the time-varying parameter vector autoregression (TVP-VAR) model, our empirical results show that the impact of CPU, GPR, EPU, and EMV on OVX is time-varying and heterogeneous due to these uncertainty measures containing different information content. In particular, the CPU has become increasingly important for triggering oil market fear since the recent Paris Agreement. During the COVID-19 pandemic, CPU, EPU, and EMV, rather than GPR, play a prominent role in increasing oil market fear. © 2023 Elsevier Ltd

Co-Evolutionary Location-Routing Model of Medical Isolation Areas for Major Public Health Emergencies Considering the Uncertainty of Future Traffic Environment

With the continuous development of the COVID-19 pandemic, the selection of locations for medical isolation areas has not always been optimal for the timely transportation of infected people, or those suspected of being infected. This has resulted in failure to control the rate of spread of infection cases in time. To address this problem, this paper proposes a co-evolutionary location-routing optimization (CELRO) model of medical isolation areas for use in major public health emergencies to develop a rapid location-routing scheme for epidemic isolation, including the selection of locations of medical isolation facilities per area and the optimal route per vehicle to each infected person. Specifically, this paper solves the following two sub-problems: (i) calculate the shortest transportation times and corresponding routes from any medical isolation area to any person infected or suspected of being infected, and (ii) calculate the location scheme for distribution of isolation areas. Different from previous studies, the vehicle operating characteristics and the interference of uncertainty of the traffic environment are considered in the proposed model. To find an appropriate scheme for location of medical isolation areas with the shortest travel times, a co-evolutionary clustering algorithm (CECA), which is a combination of some separated evolutionary programming operations, is proposed to solve the model. Various network sizes and uncertainty combinations are used to design some comparative tests, which aim to verify the effectiveness of the proposed model. In the experiment section, CELRO reduced travel time by at least 14% compared with other methods. This finding can provide an effective theoretical basis for optimizing the spatial layout of medical isolation areas or the location planning of new medical facilities. © National Academy of Sciences.

Wavelet analysis of S&P 500, oil prices, and economic policy uncertainty

We collect a total of 1830 data from January 2020 to June 2022 and use R for data processing and wavelet analysis. Moreover, we analyze the interactions between the COVID-19 pandemic, the Russian-Ukrainian war, crude oil price, the S&P 500 and economic policy uncertainty within a time-frequency frame work. As a result that the COVID-19 pandemic and the Russian-Ukrainian war has the extraordinary effects on the three indexes and the effect of the Russian- Ukrainian war on the crude oil price and US stock price higher than on the US economic uncertainty. © COPYRIGHT SPIE.

Heart Rate Evaluation by Smartphone: An Overview

In this paper, an overview of the smartphone measurement methods for Heart Rate (HR) and Heart Rate Variability (HRV) is presented. HR and HRV are important vital signs to be evaluated and monitored especially in a sudden heart crisis and in the case of COVID-19. Unlike other specific medical devices, the smartphone can always be present with a person, and it is equipped with sensors that can be used to estimate or acquire such vital signs. Furthermore, their computation and connection capabilities make them suitable for Internet of Things applications. Although in the literature many interesting solutions for evaluating HR and HRV are proposed, often a lack in the analysis of the measurement uncertainty, the description of the measurement procedure for their validation, and the use of a common gold standard for testing all of them is highlighted. The lack of standardization in experimental protocol, processing methodology, and validation procedures, impacts the comparability of results and their general validity. To stimulate the research activities to fill this gap, the paper gives an analysis of the most recent literature together with a logical classification of the measurement methods by highlighting their main advantages and disadvantages from a metrological point of view together with the description of the measurement methods and instruments proposed by authors for their validation. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

Novel COVID-19 Outbreak and Global Uncertainty in the Top-10 Affected Countries: Evidence from Wavelet Coherence Approach

This study explores the association of novel COVID-19 with the dominant financial assets, global uncertainty, commodity prices, and stock markets of the top ten corona-affected countries. We employ a wavelet coherence technique to unearth this linkage using daily data of COVID-19 deaths and reported cases from 1 January 2020 until 26 February 2021. The study finds a weak coherence between COVID-19 and global uncertainty variables in the short and medium term, while a strong positive correlation has been witnessed in the long run. The COVID-19 cases impact the stock markets in the short and medium term, while no significant impact is reported in the long run. On the other hand, a substantial impact of the COVID-19 outbreak has also been found on the exchange rate. In addition, the real asset market, such as gold, remains more stable during the COVID-19 outbreak. Thus, the study recommends that investors and portfolio managers should add such assets to their investment options to safeguard the excessive risk and downside momentum of the equity market. The study also has implications for regulators who are concerned with the neutrality of the COVID-19 effect and market stability. © 2023 by the authors.

Measuring the Impact of COVID-19 on Hospital Care Pathways

Care pathways in hospitals around the world reported significant disruption during the recent COVID-19 pandemic but measuring the actual impact is more problematic. Process mining can be useful for hospital management to measure the conformance of real-life care to what might be considered normal operations. In this study, we aim to demonstrate that process mining can be used to investigate process changes associated with complex disruptive events. We studied perturbations to accident and emergency (A &E) and maternity pathways in a UK public hospital during the COVID-19 pandemic. Co-incidentally the hospital had implemented a Command Centre approach for patient-flow management affording an opportunity to study both the planned improvement and the disruption due to the pandemic. Our study proposes and demonstrates a method for measuring and investigating the impact of such planned and unplanned disruptions affecting hospital care pathways. We found that during the pandemic, both A &E and maternity pathways had measurable reductions in the mean length of stay and a measurable drop in the percentage of pathways conforming to normative models. There were no distinctive patterns of monthly mean values of length of stay nor conformance throughout the phases of the installation of the hospital's new Command Centre approach. Due to a deficit in the available A &E data, the findings for A &E pathways could not be interpreted. © 2023, The Author(s).

How Videoconferencing and its Affordances Transformed Teaching in Schools During COVID-19 Pandemic

The COVID-19 pandemic has brought about major changes in digitization in many areas of life and professions. New areas were digitized almost overnight, the school system in Germany was no exception leading to a demand for videoconferencing tools and communication platforms. These technologies have many different functionalities that need to be discovered, explored, and exploited by the user. Given the disruptive events that the COVID pandemic brought to us, this paper aims to shed light on how the dynamics of discovery, exploration, and exploitation unfolds. We use a functional affordance theory perspective to analyze and understand how user learn to use new technologies. To do this, we conducted an exploratory case-study-based research design including interviews with teachers from various schools to analyze how they appropriate new technologies to develop an explanatory theoretical model. © 2023 IEEE Computer Society. All rights reserved.

Assessment of effective reactive power reserve in power system networks under uncertainty applying coronavirus herd immunity optimizer (CHIO) for operation simulation

The Reactive Power Reserve (RPR) is a very important indicator for voltage stability and is sensitive to the operating conditions of power systems. Thorough understanding of RPR, specifically Effective Reactive Reserve (ERR) under intermittent Wind Power (WP) and uncertain demand is essential and key focus of this research. Hence, a stochastic multivariate ERR assessment and optimization problem is introduced here. The proposed problem is solved in three stages: modeling of multivariate uncertainty, studying the stochastic behavior of ERR and optimizing ERR. The volatilities associated with WP generation and consumer demand are modeled explicitly, and their probability distribution function is discretized to accommodate structural uncertainty. A combined load modeling approach is introduced and extended further to accommodate multi-variability. The impact of these uncertainties on ERR is assessed thoroughly on modified IEEE 30 and modified Indian 62 bus system. A non-linear dynamic stochastic optimization problem is formulated to maximize the expected value of ERR and is solved using ‘Coronavirus Herd Immunity Optimizer (CHIO)'. The impact of the proposed strategy on stability indices like the L-index, Proximity Indicator (PI) are analyzed through various case studies. Further, the effectiveness of the proposed approach is also compared with the existing mean value approach. Additionally, the performance of CHIO is confirmed through exhaustive case studies and comparisons. © 2023 Elsevier B.V.

Modelling Short-Term Health Effects in Milan Area Due to Lockdown Reduced Emissions: Combined Uncertainty Analysis from Estimated NO2 Levels and Exposure–Response Functions

To contain the spread of the COVID-19 pandemic, several governments declared lockdowns. The reduction of human activities linked with mobility restriction caused an unprecedented drop in emissions, especially in the road transport sector. This study aims to evaluate the uncertainty of short-term health effects (i.e. avoided hospital admission (AHA) associated to NO2 ambient concentrations) derived from the change in air quality (AQ) due to lockdown. The CAMx-WRF modelling suite is applied for a series of nested domains using EMEP and Lombardy region emission inventories. The health impact analysis is focused on a 70 × 70 km domain centered on Milan metropolitan area with 1-km resolution, from February 24th 2020 to April 30th 2020. Two simulations, Business as usual (BAU) and lockdown scenario (LOCK), are carried out and results are compared with air quality monitoring data to assess the model uncertainty. Health effects for the difference between LOCK and BAU simulation are evaluated for NO2 following the WHO Health risks of air pollution in Europe (HRAPIE) project recommendation. The combined effect of both modelled concentrations and exposure–response functions (ERF) on the uncertainty of calculated AHA is then evaluated for different air quality station types. We find that the ERF is the major cause for uncertainty for Urban and suburban AQ stations, measured as the size of the 95% confidence interval (CI) of the estimated number of AHA. The AQ uncertainty of the BAU scenario has a lower impact on AHA CI than for the LOCK scenario. When comparing results according to station type, the lowest AHA uncertainty is obtained for urban background stations while the highest for rural background stations. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Three Mathematical Models for COVID-19 Prediction

The COVID-19 outbreak was a major event that greatly impacted the economy and the health systems around the world. Understanding the behavior of the virus and being able to perform long-term and short-term future predictions of the daily new cases is a working field for machine learning methods and mathematical models. This paper compares Verhulst's, Gompertz´s, and SIR models from the point of view of their efficiency to describe the behavior of COVID-19 in Spain. These mathematical models are used to predict the future of the pandemic by first solving the corresponding inverse problems to identify the model parameters in each wave separately, using as observed data the daily cases in the past. The posterior distributions of the model parameters are then inferred via the Metropolis–Hastings algorithm, comparing the robustness of each prediction model and making different representations to visualize the results obtained concerning the posterior distribution of the model parameters and their predictions. The knowledge acquired is used to perform predictions about the evolution of both the daily number of infected cases and the total number of cases during each wave. As a main conclusion, predictive models are incomplete without a corresponding uncertainty analysis of the corresponding inverse problem. The invariance of the output (posterior prediction) with respect to the forward predictive model that is used shows that the methodology shown in this paper can be used to adopt decisions in real practice (public health). © 2023 by the authors.